In November and December 2012, 6 patients at a hemodialysis clinic were given a diagnosis of new hepatitis C virus (HCV) infection.

To investigate the outbreak to identify risk factors for transmission.

A case patient was defined as a patient who was HCV-antibody negative on clinic admission but subsequently was found to be HCV-antibody positive from January 1, 2008, through April 30, 2013. Patient charts were reviewed to identify and describe case patients. The hypervariable region 1 of HCV from infected patients was tested to assess viral genetic relatedness. Infection control practices were evaluated via observations. A forensic chemiluminescent agent was used to identify blood contamination on environmental surfaces after cleaning.

Eighteen case patients were identified at the clinic from January 1, 2008, through April 30, 2013, resulting in an estimated 16.7% attack rate. Analysis of HCV quasispecies identified 4 separate clusters of transmission involving 11 case patients. The case patients and previously infected patients in each cluster were treated in neighboring dialysis stations during the same shift, or at the same dialysis station on 2 consecutive shifts. Lapses in infection control were identified. Visible and invisible blood was identified on multiple surfaces at the clinic.

Epidemiologic and laboratory data confirmed transmission of HCV among numerous patients at the dialysis clinic over 6 years. Infection control breaches were likely responsible. This outbreak highlights the importance of rigorous adherence to recommended infection control practices in dialysis settings.

Infect. Control Hosp. Epidemiol. 2016;37(2):125–133

Hepatitis C virus (HCV) is a major cause of chronic liver disease worldwide. A patient was recently found to be HCV seropositive during hemodialysis follow-up.

To determine whether nosocomial transmission had occurred and which viral populations were transmitted.

HCV transmission case.

A dialysis unit in a French hospital.

Molecular and epidemiologic investigations were conducted to determine whether 2 cases were related. Risk analysis and auditing procedures were performed to determine the transmission pathway(s).

Sequence analyses of the NS5b region revealed a 5a genotype in the newly infected patient. Epidemiologic investigations suggested that a highly viremic genotype 5a HCV-infected patient who underwent dialysis in the same unit was the source of the infection. Phylogenetic analysis of NS5b and hypervariable region-1 sequences revealed a genetically related virus (>99.9% nucleotide identity). Deep sequencing of hypervariable region-1 indicated that HCV quasispecies were found in the source whereas a single hypervariable region-1 HCV variant was found in the newly infected patient, and that this was identical to the major variant identified in the source patient. Risk analysis and auditing procedures were performed to determine the transmission pathway(s). Nosocomial patient-to-patient transmission via healthcare workers’ hands was the most likely explanation. In our dialysis unit, this unique incident led to the adjustment of infection control policy.

The data support transmission of a unique variant from a source with a high viral load and genetic diversity. This investigation also underlines the need to periodically evaluate prevention and control practices.

Infect. Control Hosp. Epidemiol. 2016;37(2):134–139

Central-line–associated bloodstream infection (CLABSI) rate is an important quality measure, but it suffers from subjectivity and interrater variability, and decreasing national CLABSI rates may compromise its power to discriminate between hospitals. This study evaluates hospital-onset bacteremia (HOB, ie, any positive blood culture obtained 48 hours post admission) as a healthcare-associated infection–related outcome measure by assessing the association between HOB and CLABSI rates and comparing the power of each to discriminate quality among intensive care units (ICUs).

In this multicenter study, ICUs provided monthly CLABSI and HOB rates for 2012 and 2013. A Poisson regression model was used to assess the association between these 2 rates. We compared the power of each measure to discriminate between ICUs using standardized infection ratios (SIRs) with 95% confidence intervals (CIs). A measure was defined as having greater power to discriminate if more of the SIRs (with surrounding CIs) were different from 1.

In 80 ICUs from 16 hospitals in the United States and Canada, a total of 663 CLABSIs, 475,420 central line days, 11,280 HOBs, and 966,757 patient days were reported. An absolute change in HOB of 1 per 1,000 patient days was associated with a 2.5% change in CLABSI rate (P<.001). Among the 80 ICUs, 20 (25%) had a CLABSI SIR and 60 (75%) had an HOB SIR that was different from 1 (P<.001).

Change in HOB rate is strongly associated with change in CLABSI rate and has greater power to discriminate between ICU performances. Consideration should be given to using HOB to replace CLABSI as an outcome measure in infection prevention quality assessments.

Infect. Control Hosp. Epidemiol. 2016;37(2):143–148

To develop a probabilistic method for measuring central line–associated bloodstream infection (CLABSI) rates that reduces the variability associated with traditional, manual methods of applying CLABSI surveillance definitions.

Multicenter retrospective cohort study of bacteremia episodes among patients hospitalized in adult patient-care units; the study evaluated presence of CLABSI.

Hospitals that used SafetySurveillor software system (Premier) and who also reported to the Centers for Disease Control and Prevention’s National Healthcare Safety Network (NHSN).

Patients were identified from a stratified sample from all eligible blood culture isolates from all eligible hospital units to generate a final set with an equal distribution (ie, 20%) from each unit type. Units were divided a priori into 5 major groups: medical intensive care unit, surgical intensive care unit, medical-surgical intensive care unit, hematology unit, or general medical wards.

Episodes were reviewed by 2 experts, and a selection of discordant reviews were re-reviewed. Data were joined with NHSN data for hospitals for in-plan months. A predictive model was created; model performance was assessed using the c statistic in a validation set and comparison with NHSN reported rates for in-plan months.

A final model was created with predictors of CLABSI. The c statistic for the final model was 0.75 (0.68–0.80). Rates from regression modeling correlated better with expert review than NHSN-reported rates.

The use of a regression model based on the clinical characteristics of the bacteremia outperformed traditional infection preventionist surveillance compared with an expert-derived reference standard.

Infect. Control Hosp. Epidemiol. 2016;37(2):149–155

Existing knowledge of emergency department (ED) catheter-associated urinary tract infection (CAUTI) prevention is limited. We aimed to describe the motivations, perceived risks for CAUTI acquisition, and strategies used to address CAUTI risk among EDs that had existing CAUTI prevention programs.

In this qualitative comparative case study, we enrolled early-adopting EDs, that is, those using criteria for urinary catheter placement and tracking the frequency of catheters placed in the ED. At 6 diverse facilities, we conducted 52 semistructured interviews and 9 focus groups with hospital and ED participants.

All ED CAUTI programs originated from a hospitalwide focus on CAUTI prevention. Staff were motivated to address CAUTI because they believed program compliance improved patient care. ED CAUTI prevention was perceived to differ from CAUTI prevention in the inpatient setting. To identify areas of ED CAUTI prevention focus, programs examined ED workflow and identified 4 CAUTI risks: (1) inappropriate reasons for urinary catheter placement; (2) physicians’ limited involvement in placement decisions; (3) patterns of urinary catheter overuse; and (4) poor insertion technique. Programs redesigned workflow to address risks by (1) requiring staff to specify the medical reason for catheter at the point of order entry and placement; (2) making physicians responsible for determining catheter use; (3) using catheter alternatives to address patterns of overuse; and (4) modifying urinary catheter insertion practices to ensure proper placement.

Early-adopting EDs redesigned workflow to minimize catheter use and ensure proper insertion technique. Assessment of ED workflow is necessary to identify and modify local practices that may increase CAUTI risk.

Infect. Control Hosp. Epidemiol. 2016;37(2):156–162

To compare the accuracy of surveillance of severe sepsis using electronic health record clinical data vs claims and to compare incidence and mortality trends using both methods.

We created an electronic health record–based surveillance definition for severe sepsis using clinical indicators of infection (blood culture and antibiotic orders) and concurrent organ dysfunction (vasopressors, mechanical ventilation, and/or abnormal laboratory values). We reviewed 1,000 randomly selected medical charts to characterize the definition’s accuracy and stability over time compared with a claims-based definition requiring infection and organ dysfunction codes. We compared incidence and mortality trends from 2003–2012 using both methods.

Two US academic hospitals.

Adult inpatients.

The electronic health record–based clinical surveillance definition had stable and high sensitivity over time (77% in 2003–2009 vs 80% in 2012, P=.58) whereas the sensitivity of claims increased (52% in 2003–2009 vs 67% in 2012, P=.02). Positive predictive values for claims and clinical surveillance definitions were comparable (55% vs 53%, P=.65) and stable over time. From 2003 to 2012, severe sepsis incidence imputed from claims rose by 72% (95% CI, 57%–88%) and absolute mortality declined by 5.4% (95% CI, 4.6%–6.7%). In contrast, incidence using the clinical surveillance definition increased by 7.7% (95% CI, −1.1% to 17%) and mortality declined by 1.7% (95% CI, 1.1%–2.3%).

Sepsis surveillance using clinical data is more sensitive and more stable over time compared with claims and can be done electronically. This may enable more reliable estimates of sepsis burden and trends.

Infect. Control Hosp. Epidemiol. 2016;37(2):163–171

To report the International Nosocomial Infection Control Consortium surveillance data from 40 hospitals (20 cities) in India 2004–2013.

Surveillance using US National Healthcare Safety Network’s criteria and definitions, and International Nosocomial Infection Control Consortium methodology.

We collected data from 236,700 ICU patients for 970,713 bed-days

Pooled device-associated healthcare-associated infection rates for adult and pediatric ICUs were 5.1 central line–associated bloodstream infections (CLABSIs)/1,000 central line–days, 9.4 cases of ventilator-associated pneumonia (VAPs)/1,000 mechanical ventilator–days, and 2.1 catheter-associated urinary tract infections/1,000 urinary catheter–days

In neonatal ICUs (NICUs) pooled rates were 36.2 CLABSIs/1,000 central line–days and 1.9 VAPs/1,000 mechanical ventilator–days

Extra length of stay in adult and pediatric ICUs was 9.5 for CLABSI, 9.1 for VAP, and 10.0 for catheter-associated urinary tract infections. Extra length of stay in NICUs was 14.7 for CLABSI and 38.7 for VAP

Crude extra mortality was 16.3% for CLABSI, 22.7% for VAP, and 6.6% for catheter-associated urinary tract infections in adult and pediatric ICUs, and 1.2% for CLABSI and 8.3% for VAP in NICUs

Pooled device use ratios were 0.21 for mechanical ventilator, 0.39 for central line, and 0.53 for urinary catheter in adult and pediatric ICUs; and 0.07 for mechanical ventilator and 0.06 for central line in NICUs.

Despite a lower device use ratio in our ICUs, our device-associated healthcare-associated infection rates are higher than National Healthcare Safety Network, but lower than International Nosocomial Infection Control Consortium Report.

Infect. Control Hosp. Epidemiol. 2016;37(2):172–181

Public reporting of hospital quality data is a key element of US healthcare reform. Data for hospital-acquired infections (HAIs) are especially complex.

To assess interpretability of HAI data as presented on the Centers for Medicare and Medicaid Services Hospital Compare website among patients who might benefit from access to these data.

We randomly selected inpatients at a large tertiary referral hospital from June to September 2014. Participants performed 4 distinct tasks comparing hypothetical HAI data for 2 hospitals, and the accuracy of their comparisons was assessed. Data were presented using the same tabular formats used by Centers for Medicare and Medicaid Services. Demographic characteristics and healthcare experience data were also collected.

Participants (N=110) correctly identified the better of 2 hospitals when given written descriptions of the HAI measure in 72% of the responses (95% CI, 66%–79%). Adding the underlying numerical data (number of infections, patient-time, and standardized infection ratio) to the written descriptions reduced correct responses to 60% (55%–66%). When the written HAI measure description was not informative (identical for both hospitals), 50% answered correctly (42%–58%). When no written HAI measure description was provided and hospitals differed by denominator for infection rate, 38% answered correctly (31%–45%).

Current public HAI data presentation methods may be inadequate. When presented with numeric HAI data, study participants incorrectly compared hospitals on the basis of HAI data in more than 40% of the responses. Research is needed to identify better ways to convey these data to the public.

Infect. Control Hosp. Epidemiol. 2016;37(2):182–187

Carriers of carbapenem-resistant Enterobacteriaceae (CRE) are often readmitted, exposing patients to CRE cross-transmission.

To identify predictors of persistent CRE carriage upon readmission, directing a risk prediction score.

Retrospective cohort study.

University-affiliated general hospital.

A cohort of 168 CRE carriers with 474 readmissions.

The primary and secondary outcomes were CRE carriage status at readmission and length of CRE carriage. Predictors of persistent CRE carriage upon readmission were analyzed using a generalized estimating equations (GEE) multivariable model. Readmissions were randomly divided into derivation and validation sets. A CRE readmission score was derived to predict persistent CRE carriage in 3 risk groups: high, intermediate, and low. The discriminatory ability of the model and the score were expressed as C statistics.

CRE carrier status persisted for 1 year in 33% of CRE carriers. Positive CRE status was detected in 202 of 474 readmissions (42.6%). The following 4 variables were associated with persistent CRE carriage at readmission: readmission within 1 month (odds ratio [OR], 6.95; 95% confidence interval [CI], 2.79–17.30), positive CRE status on preceding admission (OR, 5.46; 95% CI, 3.06–9.75), low Norton score (OR, 3.07; 95% CI, 1.26–7.47), and diabetes mellitus (OR, 1.84; 95% CI, 0.98–3.44). The C statistics were 0.791 and 0.789 for the derivation set (n=322) model and score, respectively, and the C statistic was 0.861 for the validation set of the score (n=152). The rates of CRE carriage at readmissions (validation set) for the groups with low, intermediate, and high scores were 8.6%, 38.9%, and 77.6%, respectively.

CRE carrier state commonly persists upon readmission, and this risk can be estimated to guide screening policy and infection control measures.

Infect. Control Hosp. Epidemiol. 2016;37(2):188–196

Staphylococcus aureus is a common cause of bacterial infections worldwide. It is most commonly carried in and transmitted from the anterior nares. Hosts are known to vary in their proclivity for S. aureus nasal carriage and may be divided into persistent carriers, intermittent carriers, and noncarriers, depending on duration of carriage. Mathematical models of S. aureus to predict outcomes of interventions have, however, typically assumed that all individuals are equally susceptible to colonization.

To characterize biases created by assuming a homogeneous host population in estimating efficacy of control interventions.

Mathematical model.

We developed a model of S. aureus carriage in the healthcare setting under the homogeneous assumption as well as a heterogeneous model to account for the 3 types of S. aureus carriers. In both models, we calculated the equilibrium carriage prevalence to predict the impact of control measures (reducing contact and decolonization).

The homogeneous model almost always underestimates S. aureus transmissibility and overestimates the impact of intervention strategies in lowering carriage prevalence compared to the heterogeneous model. This finding is generally consistent regardless of changes in model setting that vary the proportions of various carriers in the population and the duration of carriage for these carrier types.

Not accounting for host heterogeneity leads to systematic and substantial biases in predictions of the effects of intervention strategies. Further understanding of the clinical impacts of heterogeneity through modeling can help to target control measures and allocate resources more efficiently.

Infect. Control Hosp. Epidemiol. 2016;37(2):197–204